CMC Blog

We spoke to Dr. Hsin-Hsuan Meg Lee, a professor here
at ESCP Europe specialising in self-presentation and digital
identity. She is currently teaching a class called Creative
Analytics. Her degrees in zoology and animal behaviour formed the
bases for her scientific research training. Indeed, when she made
the transition to marketing, she carried the habit and love of
observation with her. She maintains that this is also why
observation is still her favourite research method, a method on the
rise since the emergence of the digital culture.

The Creative Analytics module is not about the debate
and paradox between creativity and analytics as is often portrayed
in popular media.

Creativity is a crucial element in marketing
analytics. We don't have to think of it as either the creative
route or the analytical route; the two work hand in hand. So in
order to make analytics work to achieve your goals, you'll need to
seek out a creative approach to gather relevant information,
organize data, develop the research model, interpret the results,
and to finally come up with the implementation plan. At every step
of the way, managers need to be creative in finding the best
research tools and making decisions in prioritizing these tactics
(as analytics often involve compromise).

While the statistical process itself can also be
really creative, the Creative Analytics course really
emphasises the managerial decisions taken along the way. It's
basically an analytics course (almost) without statistics.

When and why do companies need to consider
marketing analytics? Are smaller companies or startups who may not
have the resources really losing out by not conducting more
research?

To answer this question we need to define "analytics."
For me, marketing analytics is the process of gathering,
organising, transforming and interpreting data in a way that can
assist companies to make better marketing decisions. It's important
for all companies to consider marketing analytics, if they value
marketing.

That said, analytics vary greatly in their scope and
scale, and companies may have different levels of capacity. Indeed,
some companies may be concerned about the resources required to
take on analytics. Companies shouldn't conduct research for the
sake of doing it, but if data is just lying around, it's a waste
not to use it.

In your opinion, is there such a thing as
conducting too much research? Is it okay to take a risk even though
the research to back up the idea isn't there?

I believe all analytics and research involve a human
element. Managers eventually are the ones to decide what goes into
a research assignment, a model, a survey, or an experiment. What
should be measured and how should the data be interpreted and
implemented are all determined by managers. There aren't any faults
in science, as data is just data.

You can never have too much data. Information is the
key to any business. That said, too much information can
potentially jeopardise the decision-making process. I think there
is danger in being overly data-driven and ignoring the fact that
all analytics should be managerially relevant.

Consumer research should be complementary to creative
ideas within a firm. Insights often come from observations and
reflections. Observation is a form of research, whether it's done
scientifically or casually. The reliability of the results may be
different--the risk level might differ, but observation should be
taken into account all the same as it speaks to the creativity of
companies.

Do analytics always translate to real-life
consumer needs? How do you balance what analytics are telling you
and common sense, if these two are vastly different?

I don't believe analytics always translate to
real-life consumers' needs. The quality and the reliability of the
results are influenced by the rigorousness of the process. Most of
the analytics are based on samples. Big data--large volume of
data--expands the sample size to such a degree that we could
consider it the closest to the reality we'll ever get.

However, from raw data to analytics results, there are
still several steps to take, such as organising the data and
constructing the models. Every single decision made in the process
should influence the results. From here, analysts will indicate how
certain they are about the results, which can never reach 100%
unless they were able to get the whole population in their
analysis.

So yes, there will be times when the results may seem
to contradict what you believe to be true, which all comes down to
the managers' call. You can choose to trust the predictions or not,
it's all about taking risks.

Do you think you should come up with the idea
first and then use analytics to support it, or should the analytics
drive the idea? Or both?

Blindly diving into data is risky, if not suicidal.
I've seen many results be manipulated to fit a conclusion. Just
like any decision made in business, you should never start projects
without knowing the objectives.

If the purpose is to explore, then indeed keep an open
mind and let the analytics drive some ideas. These exploratory
studies are important from time to time. If managers already know
everything, then there would be no failing businesses, right?

But marketers need to be careful with how they use
exploratory studies and make sure they conduct this kind of
research if it fits the goals of the research. If there are any
assumptions established from the start, the objective of the
research should just be set accordingly.

Too often marketers start a research project with a
pre-existing idea that they are seeking to prove and use the wrong
research tactics to test this idea. Typically, in these scenarios,
they will use exploratory methods, which end up being totally
inefficient. For example, rather than choosing to run A/B testing
to prove a certain idea/campaign might work best, a manager might
choose to run an exploratory study instead. Managers will then mold
these exploratory analytical results to fit their pre-existing
idea, or just ignore the results (which is a complete waste of
time).

How have web analytics impacted consumer
research? What are the most important metrics to look out
for?

Web analytics provide a great opportunity for
companies to observe consumers. There are of course other ways to
understand consumers, and while web analytics should play an
important role (depending on the business), they should never
substitute other legitimate methods.

I don't think there's one single metric to look for.
What is important for companies to measure greatly depends on their
purpose, the objective and the capacity of the companies. I think
believing that there's a magic formula is a dangerous belief.

Can you share an example where conducting research has
led to an impossibly creative marketing idea?

The most well-known successful example is probably
Netflix. And the most famous failing example is probably Google
Plus.

I think real success often lies in the decisions made
along the way. It's those daily decisions where you can see the
most obvious use of analytics. These decisions include whether or
not to drop the print catalogues, which customer groups to focus
on, which keywords to include in campaigns, which headlines to use
in the newsletter etc. Creativity often hides within these baby
steps.

What are your favourite free analytics
tools?

I don't really have one favourite free analytics tool. For
different purposes, I use different things, but most of them are
rather academic oriented. I'm slowly moving towards using R for
most of my own academic analytics, which is free but may not
exactly be so user-friendly.

Interested in how to become a manager who
understands how to implement creativity in marketing analytics?
Check out how ESCP Europe and
its Marketing and Creativity programmes, which will equip you with
just the tools you'll need!